This proposed project has three primary objectives. Objective 1 is to develop improved strategies for fitting more accurate classification and regression tree (i.e., CART) models. Objective 2 is to develop a formal framework to allow statistical inference on tree models. Objective 3 is to develop and distribute public-domain software that will allow applied data analysts to implement the methods we develop in the first two objectives. To meet these objectives we will integrate statistical and computational machine learning approaches. We believe our work can have a significant impact in biomedical data analysis by combining the strengths of statistics for developing objective criteria for model selection and for providing a framework for assessing and quantifying uncertainty associated with a model, with the strengths of machine learning for fitting models to large and complex datasets.